Johann Heinrich von Thünen-Institut

This study presents the first integrative assessment of polycyclic aromatic hydrocarbons (PAHs) in sediments from the Nembe mangrove ecosystem (Niger Delta, Nigeria), evaluating contamination levels, probable sources, and associated ecological and human health risks. Sediment samples collected from three stations were analysed for the 16 priority PAHs using gas chromatography-flame ionization detection (GC-FID). Fourteen PAH compounds were detected, with a mean concentration of 4728±2745mg/kg, far exceeding both the Nigerian intervention limit (4mg/kg) and international guideline values. Multivariate statistical methods were employed for source apportionment, and the 5th and 95th percentile health risks were estimated using Monte Carlo simulation. Diagnostic ratios, and correlation analysis indicated mixed petrogenic (oil-derived) and pyrogenic (combustion-related) inputs, consistent with oil spillage events and biomass burning activities. Ecological risk assessment showed that several PAHs exceeded the effect range-median (ER-M) thresholds, suggesting a high probability of adverse biological effects. The total 5th and 95th Percentile carcinogenic risk were 2.20×10-3 and 3.73×10-3 for children, and 1.60×10-3 and 2.77×10-3 for adults, respectively, all exceeding the United States Environmental Protection Agency maximum acceptable risk threshold (>1×10-4). Dermal contact and ingestion were identified as the dominant exposure pathways. Overall, the findings reveal severe PAH contamination in Nembe mangrove sediments, with substantial ecological and public health implications. Targeted public health interventions, stricter regulation of PAH sources, and sustained seasonal monitoring are urgently needed to mitigate oil spills, reduce human exposure, and guide remediation and policy development.

Scots pine (Pinus sylvestris L.) spans most of Eurasia, yet southern and mountainous populations may retain distinctive genetic components shaped by long-term isolation and complex postglacial dynamics. We genotyped 186 trees from four Scots pine stands in Armenia (AM1-AM4) and reference stands from Germany, Russia and Montenegro with the PiSy50k SNP array and integrated these data with published European array datasets from Finland, Poland and the Baltic region. After quality checks and conservative SNP filtering, 627 individuals from 47 populations and 3659 SNP loci were retained. Within-population diversity was generally high; Armenian stands AM2–AM4 were among the most diverse, whereas AM1 showed reduced diversity and the highest differentiation relative to the remainder of the dataset (FST vs. rest = 0.0047). Direct pairwise FST and hierarchical AMOVA confirmed pronounced heterogeneity among Armenian stands, with AM1 the most differentiated stand, AM2 and AM4 closest to the broader Eurasian background, and AM3 intermediate. Principal component analysis (PC1 = 1.42%, PC2 = 0.76%) again separated AM1 strongly from all non-Armenian samples, while AM2 overlapped with the central/eastern European cluster and AM3 and AM4 combined continental-like and AM1-like individuals. Structure-like inference with LEA/sNMF showed a broad cross-entropy plateau from approximately K = 4 to K = 6; we therefore use K = 5 as a practical summary, which highlighted a dominant AM1-associated ancestry component and variable continental admixture in AM2–AM4. KING kinship estimates provided little evidence for within-stand family clustering in Armenian stands; no second-degree-or-closer pairs were observed in AM1–AM4. Together, the results reveal pronounced heterogeneity among Armenian Scots pine stands and identify AM1 as a highly differentiated but unresolved genomic component, providing a genomic baseline to support conservation planning, provenance evaluation and the management of forest reproductive material in the Lesser Caucasus.

Biogeochemical models are useful tools for modeling nitrous oxide (N2O) emissions from agricultural mineral soils. However, most biogeochemical models assume that conditions favoring N2O producing reactions, nitrification and denitrification, are spatially homogeneous distributed in soil. Recent studies have shown, that conditions favoring N2O producing, like the availability of easy degradable organic carbon, nitrate and ammonium and the establishment of anaerobic conditions are often concentrated in hot spots around particular organic matter originating from crop residues and organic amendments. In contrast to common biogeochemical models, spatially explicit models are required to better describe dynamics of N2O emissions.To address the role of spatial heterogeneity of conditions responsible for nitrification and denitrification, we introduce a model approach combining biogeochemical process descriptions at hot spots and the bulk soil. The „hot spot” part of the model includes the description of diffusive transport of solutes and gases in a spherical object combined with process descriptions like mineralization, nitrification, nitrifier denitrification and denitrification. Several instances of the hot spot module then interact with the model approach describing processes in the bulk soil.In this study, a laboratory experiment will be modeled in order to simulate the application of a single type of manure on the surface and the injection of manure into the center of a column in sandy and loess soils with water contents set at 40% and 60% WFPS.The submodel will be integrated into the DNDCv.Can model, which provides the boundary conditions and input data required for the submodel and also participates in determining the O2 concentration by explicitly calculating the diffusion from the surface to the soil layer where the hot spot is located.Our hypothesis is that the DNDCv.Can model with the hot spot submodule will be able to describe the daily dynamics of the nitrification and denitrification processes generated by the hot spot induced in the experiment more accurately, which would greatly help the further development of the model for the application of this approach in real agricultural practice.

Soil organic carbon (SOC) stocks in European croplands are declining, and increasing demand for biomass to substitute fossil fuel–based products may further exacerbate this trend. Cereal straw represents a substantial and still underutilized biomass resource; however, its additional removal may intensify SOC losses. This study quantifies the impact of increased straw removal on SOC stocks in German croplands and evaluates the potential of biochar (BC) produced from cereal straw to compensate for these losses.SOC dynamics were modelled for 1,115 cropland sites using soil and management data from the first German Agricultural Soil Inventory. Seven scenarios representing different levels of straw removal and BC application were simulated using a model ensemble combining three allometric approaches for carbon input estimation with the RothC soil carbon model.Annual straw harvest from German croplands could theoretically be tripled to 26.3 million Mg straw (dry matter). Removing all additionally harvestable straw (17.4 million Mg) reduced mean carbon inputs to cropland soils by 21% and resulted in an average SOC loss of 8.9 Mg C ha⁻¹ within 100 years. During the first 20 years, SOC stocks declined by 0.10 ± 0.20 Mg C ha⁻¹ a⁻¹, corresponding to additional emissions of 4.2 million Mg CO₂ a⁻¹ at the national scale compared to business-as-usual. Although SOC losses decreased over time, they persisted beyond 200 years and were more pronounced on clayey than on sandy soils.In contrast, converting straw to BC and applying it to the same cropland soils led to an SOC increase of 16.7 Mg C ha⁻¹ within 100 years, equivalent to a net carbon sink of 25.6 Mg C ha⁻¹ and negative emissions of 93.9 million Mg CO₂ ha⁻¹. An average BC application rate of 4.9 Mg C a⁻¹ increased SOC by 10.8 million Mg CO₂ a⁻¹ relative to business-as-usual, corresponding to approximately 10% of Germany’s current annual agricultural greenhouse gas emissions. Accounting for SOC losses due to straw removal reduced cumulative SOC gains over 100 years by 25%.Our results demonstrate that additional straw harvesting can undermine SOC stocks and climate mitigation efforts, whereas BC application from cereal straw has substantial potential to offset these losses. However, climate mitigation strategies relying on agricultural residues should integrate SOC losses from biomass removal to avoid overestimating the mitigation potential of BC systems.

Recently, many literature reviews have focused on soil inorganic carbon (SIC) and concluded that SIC should be studied as much as soil organic carbon (SOC) to complete the soil C balance at global scale. However, as SIC has been rarely studied lately, analytical methods for measuring its carbon content and 13C in calcareous soils have not been refined. Similarly, the quantification of different SIC pools remained poorly assessed. This study aimed to illustrate the diversity of SOC and SIC pools in calcareous soils. A soil collection of 160 soil samples were selected from mostly agricultural lands in Canada, Hungary, France, Germany, Italy, and Tunisia to cover a large range of SOC and SIC contents. The carbonate composition was mainly in the form of calcite and, to a lesser extent, dolomite. The natural abundance in 13C of SOC and SIC showed a small range of values for SOC (-35 to -20 ‰, median: -27‰) signalling a predominance of C3-plants, and a large range of values for the SIC (-31 to +3 ‰; median -5 ‰), indicating a possible mixture of inorganic carbon from different sources (Hazera et al. 2025). Standard procedures to quantify SOC and SIC involving pretreatments to remove one of the C forms and/or calculations could lead to both analytical errors and substantial measurement errors (Jakab et al. submitted). Thermal analysis (i.e. Rock-Eval®, RE) has been adapted to estimate SOC and SIC contents on a non-pretreated soil sample without needing statistical post-hoc corrections (Hazera et al., 2023, in press). This RE method was applied to characterize more than 400 particle-size fractions of 65 calcareous soils i.e., particulate organic matters (POM, > 50 µm) and mineral associated organic matters (MaOM, 20-50, 0-20 and 0-2 µm). The variability of C distribution in the different fractions was more pronounced for SIC than SOC: the SOC was mainly contained in the

Nitrous oxide (N2O) is one of the most relevant anthropogenic greenhouse gases, mainly produced in agricultural soils. Understanding the mechanisms responsible for N2O production and consumption is crucial for developing N2O mitigation strategies in croplands but field studies combining N2O flux measurements with isotopocule signatures and metagenomic analysis are very rare.With the aim to clarify how isotopic signatures and functional genes can help explaining N2O fluxes in a cropland, we conducted a two years study, from February 2023 to January 2025, at the Reinshof experimental farm of the University of Göttingen in Germany (51.49° N, 9.93° E). The crop sequence was sugar beet, winter wheat and winter barley.We measured N2O fluxes with eight manually-operated static chambers and gas chromatography and additionally collected samples for analysis of N2O isotopocule signatures. We applied FRAME, a three-dimensional model based on 15N site preference, bulk 15N and 18O isotopic signatures to distinguish between the source processes (bacterial denitrification, nitrifier denitrification, fungal denitrification, nitrification) and to estimate the reduction of N2O to N2.We regularly monitored soil water content, mineral nitrogen (Nmin) and dissolved organic carbon (DOC). Additionally, every four months we collected soil samples for real-time quantitative polymerase chain reaction (qPCR)-based quantification of bacterial and fungal DNA, as well as functional genes involved in nitrification (ammonia-oxidizing archaea and bacteria amoA genes) and denitrification (nirK, nirS, and nosZ clade I and II).We observed mean N2O fluxes of 19.8 µg N2O-N m-2 h-1. Individual chamber measurements ranged from -26.7 to 573.1 µg N2O-N m-2 h-1. The spatial variability between chambers within one day showed a high coefficient of variation of 123%. N2O fluxes increased after fertilization, rewetting and harvest while highest fluxes occurred after a freeze-thawing event. Cumulative fluxes showed that 0.97% of applied fertilizer N was emitted as N2O-N. We observed a significant positive effect of soil moisture on N2O fluxes, no significant effect of Nmin and a significant negative effect of DOC.Preliminary results showed that bacterial denitrification was the dominant process responsible for N2O production. As N2O fluxes increased, the proportion of bacterial denitrification increased while the proportion of nitrification decreased. Following freeze-thawing, there was more bacterial denitrification than after the fertilization events and very little fungal denitrification. Overall, the residual N2O fraction of 45.9 ± 15.0% suggested extensive nitrogen loss as N2 via denitrification. The dominance of nosZ over nirK and nirS further implied substantial conversion of nitrogen to N₂.In addition, bacterial DNA was more abundant than fungal DNA, and denitrifiers were more abundant than nitrifiers. No clear differences in processes or gene copy numbers were observed between chambers. Seasonally, gene copy numbers of most functional genes were higher during the growing season and lower in winter, consistent with higher N₂O fluxes during the growing season and lower fluxes in winter.In future analysis, we will show how soil characteristics, isotopic signatures, and functional genes jointly shape the spatial and temporal variation of the measured N2O fluxes.

Soil organic carbon (SOC) is the largest terrestrial carbon reservoir and acknowledged to play an important role in climate mitigation. It has been advocated to store additional SOC in stable forms to provide efficient climate change mitigation. For this, mineral associated organic matter (MAOM) is considered to be relatively stable and has longer residence times than faster cycling particulate organic matter. However, we require a better mechanistic understanding of microscale interactions and the controlling factors of MAOM formation. Here we used arable soils across Germany representing three texture classes (sandy, loamy and clayey), two levels of SOC contents (low and high; 7-73 mg OC g-1 soil) and consequently a range of SOC loading in the MAOM fraction (2-122 mg OC g-1 fraction). The soils were incubated for two years after an addition of highly 13C labelled (8 atom%) barley-litter and fractionated by particle size to extract the MAOM (

Agricultural soils are increasingly under pressure due to land use intensification and the ongoing effects of climate change. Current EU policies, such as the "Carbon Removals and Carbon Farming Regulation" (CRCF), aim to improve the resilience of cropland soils by carbon sequestration through climate-smart management. However, such regulations significantly increase the demand for spatiotemporal soil data to monitor and verify the effectiveness of carbon farming measures.In recent years, the analysis of remote sensing-based bare soil observations has been increasingly used to generate accurate, high-resolution maps of cropland properties, such as soil organic carbon (SOC). However, due to the lack of robust reference data and the slow-changing nature of SOC, validating temporal model performance remains challenging. In this study, we tested the extent to which spatiotemporal models based on satellite data can support wall-to-wall soil monitoring and provide information on the temporal variability of SOC in cropland soils.To achieve this, bare soil composites were derived from Landsat and Sentinel-2 data using a moving window approach and compared to Bavarian long-term soil monitoring data from 1986 to 2022. The results showed that while overall model performance was high, the validation of measured SOC trends yielded significantly lower accuracy, underlining the high uncertainty in predicting temporal soil carbon dynamics. While long-term analyses of 25+ years were necessary to detect significant SOC changes in most cases, the classification of the results revealed a low confusion rate between sites with increasing or decreasing SOC trends across the observation period.These findings are supported by recent results based on the repetition of the German agricultural soil inventory, currently being conducted at the Thünen Institute of Climate-Smart Agriculture. Although significant uncertainties remain in quantifying SOC dynamics within 10-year intervals, results can be improved by taking the plot-scale SOC variability into account. This preprocessing step not only improves the significance of spatiotemporal SOC models ("model-then-derive") but also allows for the direct prediction of SOC changes based on a "derive-then-model" approach.In summary, these results provide a first step toward an integrated soil monitoring system based on remote sensing and repeated soil sampling. While the findings demonstrate that it is possible to validate spatiotemporal SOC models using long-term sampling data, they also highlight the necessity of further improving the accuracy and applicability of the models. Based on our studies, we will further discuss the opportunities and challenges to independently validate SOC trends claimed by carbon farming schemes using remote sensing data.

Soil electrical conductivity (EC) and aggregate-size distribution are critical indicators of soil salinity risk, structural integrity, and overall soil health. We assessed the status of these properties in 188 wheat plots across nine European pedoclimatic zones to quantify the influence of climate and agricultural management. Most soils (~88%) were non-saline, 9% slightly saline, and 3% moderately saline, with the highest salinity in Mediterranean regions. EC was generally lower under organic management, reflecting higher soil organic carbon, improved porosity, and enhanced cation retention. Soils were dominated by small macroaggregates (250–2000 µm) and large microaggregates (53–250 µm), together accounting for an average of 73% of total aggregates. Climate was the primary determinant of both EC and aggregate distribution, with drier and warmer conditions promoting salinization and smaller aggregate sizes, whereas wetter conditions favored macroaggregate formation. Agricultural management had a secondary but context-dependent effect, particularly on soil aggregation, with organic farming, integrated organomineral fertilization, crop residue incorporation, and legume rotations enhancing macroaggregate formation, especially in low-SOC soils. These results indicate that pedoclimatic conditions largely shape soil salinity and structure, but adopting targeted, site-specific management practices can sustain soil health and mitigate risks related to salinity and structure, particularly under projected climate change.

ABSTRACT Achieving the EU Soil Strategy's goal of 75% healthy soils by 2030 critically relies on farmers, who are expected to act as soil stewards. This article explores the conditions external to farms that shape soil management decisions and we examine what these conditions imply for opportunities for achieving healthy soils. Drawing on a farming systems perspective, we conceptualize soil management as an outcome of interrelated ecological, economic, social, and policy dynamics, rather than individual decision‐making alone. We identify five key domains (ownership and finance, farming structure, markets and value chains, technology and data governance, and policy regimes) that jointly enable or constrain soil stewardship. We show how changes to these underlying conditions limit the abilities of individual farmers to engage in long‐term soil care. The analysis further underscores that to understand soil stewardship, we need to move beyond actor‐centric approaches toward agri‐food system‐level interventions that also consider dynamics that are beyond the reach of individual land‐users. Strengthening soil health therefore requires a reconfiguration of the institutional, financial, and technological conditions governing agricultural soil management, rather than a narrow focus on farmers' individual practices and decision making.